AWS Application Discovery Service MCP Server Integration Guide
Section A: Quick Answer & Architectural Summary
The AWS Application Discovery Service Model Context Protocol (MCP) integration bridges AI coding assistants to the AWS Application Discovery Service databases API. It exposes 10 validated endpoint operations as callable tools for Claude Desktop, Cursor, and VS Code. Configuration is managed via hosted registry at /config/amazonaws-com-discovery.json or local stdio bridge execution. Operates with zero authentication credentials out of the box. Contains 10 mutating operations (POST/PUT/DELETE); user confirmation is recommended before triggering write operations.
MCPBridge Editorial Verdict: AWS Application Discovery Service
AI coding workflows requiring programmatic access to AWS Application Discovery Service (Databases) endpoints
Low (1-2 mins)
Zero Authentication Required
Automated Spec Tracking
Claude Desktop, Cursor IDE, VS Code (Cline), Zed Editor
Read & Mutating endpoints; client confirmation and least-privilege token recommended
MCPBridge rates AWS Application Discovery Service as a standardized OpenAPI-to-MCP bridge providing structured tool definitions across 10 endpoints.
Technical Overview & Protocol Integration
The AWS Application Discovery Service, provided by Amazon Web Services (AWS), is a foundational tool designed to simplify and de-risk the complex process of migrating on-premises data center environments to the cloud. Its core capability lies in the automated discovery, collection, and analysis of critical infrastructure data, including server configurations, performance metrics, utilization patterns, and network dependencies. By deploying lightweight agents on physical servers or VMs, or by leveraging agentless discovery through existing data sources like VMware vCenter, the service constructs a detailed inventory and dependency map of the application portfolio. This data is instrumental for enterprise migration planning, enabling accurate Total Cost of Ownership (TCO) calculations for AWS migration services like AWS Migration Hub, AWS Server Migration Service, and AWS Database Migration Service. Typical use cases include data center consolidation, cloud readiness assessments, application modernization planning, and maintaining an accurate, up-to-date IT asset inventory.
When this service's API is exposed as a set of tools via the Model Context Protocol (MCP) to an AI coding assistant, it transforms from a standalone discovery console into a dynamic, programmable resource for intelligent migration orchestration. An AI agent like Claude Desktop or Cline gains the ability to directly interact with the discovery data lifecycle, moving beyond static analysis to perform real-time, contextual operations. The value lies in enabling the AI to act as a migration planner and execution coordinator, automatically enriching application portfolios with discovered infrastructure context, validating migration groupings, and updating project metadata based on live or recent scan data. This integration bridges the gap between descriptive data and actionable insight, allowing the AI to not only read but also actively manage the foundational dataset of a migration project.
A developer can instruct the AI agent to perform a variety of dynamic, integrated tasks. For example, they can command it to "Create a new application container in AWS Application Discovery Service for the 'Customer Portal' project and associate the previously discovered servers identified by their configuration IDs." The AI would execute the CreateApplication and AssociateConfigurationItemsToApplication calls sequentially. Another workflow could involve the instruction: "Generate a summary report of all exported configuration data from the last month, identify any servers with less than 20% CPU utilization, and tag those servers in the discovery service with 'LowUtilization' for later decommission analysis." This would combine DescribeExportConfigurations to find the data, process the information, and then use CreateTags to update the asset records. Furthermore, an agent could be tasked to "Clean up test data by deleting the staging application and all its associated import records," which would trigger DeleteApplications followed by BatchDeleteImportData.
Secure integration with the AWS Application Discovery Service API is paramount. Although the endpoint authentication mechanism is listed as "None," this refers to the direct HTTP endpoint signature; in practice, all API calls must be authenticated and authorized using AWS Identity and Access Management (IAM). Developers must configure the AI agent's execution environment with secure AWS credentials, preferably via an IAM role with temporary credentials (e.g., AWS STS AssumeRole) rather than long-lived access keys. The principle of least privilege is critical; the IAM policy attached to the role or user should grant only the specific permissions required for the intended tasks (e.g., discovery:CreateApplication, discovery:DescribeAgents), and should be scoped to the relevant AWS account and, if possible, specific resources. All communication should be secured with TLS, and audit logs via AWS CloudTrail should be enabled to monitor all API activity for compliance and forensic analysis.
By translating the OpenAPI 3.0 specification for AWS Application Discovery Service into native Model Context Protocol (MCP) tool definitions, developers and AI agents gain programmatic access to endpoints over stdio or HTTP transports. Every endpoint is translated into a discrete tool payload complete with input argument validation, parameter descriptions, and return type definitions.
2. Technical Specifications Matrix
System Specifications
| API Name | AWS Application Discovery Service |
| Slug Identifier | amazonaws-com-discovery |
| Category | Databases |
| Auth Method | None Required |
| Endpoint Count | 10 tools mapped |
| Spec Version | OpenAPI v2015-11-01 |
| Transport Type | STDIO |
| Publisher Source | auto |
3. Multi-Client Installation Matrix
Copy and paste these pre-formatted JSON snippets into your MCP client configuration files.
Claude Desktop
Add to claude_desktop_config.json
{
"mcpServers": {
"amazonaws-com-discovery": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-openapi",
"https://api.apis.guru/v2/specs/amazonaws.com/discovery/2015-11-01/openapi.json"
],
"env": {
"AWS_APPLICATION_DISCOVERY_SERVICE_API_KEY": "your_aws_application_discovery_service_api_key"
}
}
}
}Cursor IDE
Settings → MCP Servers → Add Hosted Config
{
"mcpServers": {
"amazonaws-com-discovery": {
"url": "https://mcpbridge.org/config/amazonaws-com-discovery.json"
}
}
}Saves as .cursor/mcp.json in the download. Move it to your project root.
VS Code / Cline
Use with MCP extension config
{
"mcpServers": {
"amazonaws-com-discovery": {
"url": "https://mcpbridge.org/config/amazonaws-com-discovery.json"
}
}
}4. Security Architecture & Credentials Reference
Key parameters and credential variable mappings for AWS Application Discovery Service.
Security Considerations & Sandbox Guidance: AWS Application Discovery Service
Authorization credential isolation, least privilege boundaries, and container sandboxing options.
None Required
Read & Mutating Operations
Local MCP bridge process making outbound HTTPS requests to upstream API
Isolation & Principle of Least Privilege
Ensure outbound network access to the API endpoint is permitted. Use restricted API tokens with minimal read/write scopes.
Actionable Operational Guidelines
- Verify network firewall rules allow outbound traffic to upstream API endpoints.
- Review arguments for mutating endpoints (/#X-Amz-Target=AWSPoseidonService_V2015_11_01.AssociateConfigurationItemsToApplication, /#X-Amz-Target=AWSPoseidonService_V2015_11_01.BatchDeleteImportData, /#X-Amz-Target=AWSPoseidonService_V2015_11_01.CreateApplication) before execution.
- Apply token rate limits and monitor usage in your provider dashboard to prevent unexpected quota consumption.
| Variable Name | Required | Example Value |
|---|---|---|
| AWS_APPLICATION_DISCOVERY_SERVICE_API_KEY | REQUIRED | your_aws_application_discovery_service_api_key |
5. Endpoints & Tool Schemas Matrix
Search and inspect the 10 tool signatures mapped from OpenAPI.
Executable Code Integration Examples
Call AWS Application Discovery Service endpoints via cURL, TypeScript, or Python REST SDKs.
curl -X POST "https://api.apis.guru/v2/specs/amazonaws.com/discovery/2015-11-01/#X-Amz-Target=AWSPoseidonService_V2015_11_01.AssociateConfigurationItemsToApplication" \ -H "Content-Type: application/json" \ # No auth required
Concrete Real-World Use Cases for AWS Application Discovery Service
Practical multi-step agentic workflows and prompt directives demonstrating concrete developer outcomes.
Automated Contextual Workflow Integration
A developer can instruct the AI agent to perform a variety of dynamic, integrated tasks. For example, they can command it to "Create a new application container in AWS Application Discovery Service for the 'Customer Portal' project and associate the previously discovered servers identified by their configuration IDs." The AI would execute the `CreateApplication` and `AssociateConfigurationItemsToApplication` calls sequentially. Another workflow could involve the instruction: "Generate a summary report of all exported configuration data from the last month, identify any servers with less than 20% CPU utilization, and tag those servers in the discovery service with 'LowUtilization' for later decommission analysis." This would combine `DescribeExportConfigurations` to find the data, process the information, and then use `CreateTags` to update the asset records. Furthermore, an agent could be tasked to "Clean up test data by deleting the staging application and all its associated import records," which would trigger `DeleteApplications` followed by `BatchDeleteImportData`.
- AI assistant inspects prompt context and selects relevant tool
- Validates parameter payload against OpenAPI JSON Schema
- Executes tool call and formats structured API response
Automated Mutation & Resource Creation
Execute state changes and create records through POST operations like "/#X-Amz-Target=AWSPoseidonService_V2015_11_01.AssociateConfigurationItemsToApplication" with parameter validation.
- Agent constructs validated request body matching schema
- Prompts user for execution confirmation
- Executes tool and confirms response status
Good Fit vs. Poor Fit Criteria for AWS Application Discovery Service
Architectural guidelines to determine when to adopt this integration and when to explore alternatives.
When to Choose / Good Fit
- AI coding assistants in Claude Desktop or Cursor requiring structured tool access to AWS Application Discovery Service.
- Developers who want standardized OpenAPI-to-MCP translation without building custom server code.
- Workflows that benefit from automated parameter validation against official OpenAPI 3.0 schemas.
- Teams seeking zero-maintenance hosted JSON configurations for easy distribution.
When to Avoid / Poor Fit
- Ultra-high frequency data ingestion exceeding typical LLM context windows and token rate limits.
- Unattended autonomous agent loops with write access where human approval of mutations is mandatory.
- Environments lacking outbound internet access to upstream AWS Application Discovery Service API servers.
Verification & Evidence Audit: AWS Application Discovery Service
OpenAPI 3.0 specification parsed and validated via automated build pipeline.
Independent Evidence Checks
Valid specification version 2015-11-01 with 10 endpoints indexed.
No authentication required.
JSON Schemas mapped to MCP tools/call standard format.
Automated schema validation only; live upstream API calls require developer credentials.
Project Health & Maintenance Audit: AWS Application Discovery Service
ActiveActivity & Cadence
Transparent Quality Score Breakdown
Alternatives & Comparison Table (Databases)
Comparative trade-offs between AWS Application Discovery Service and similar ecosystem tools in the Databases category.
| Option | Best For | Main Difference vs. AWS Application Discovery Service | Setup / Runtime | Explore |
|---|---|---|---|---|
| Amazon CloudWatch Application Insights | Developers needing Databases operations with 10 tools | 10 endpoints vs 10 endpoints | auto / v2018-11-25 | View → |
| Amazon DocumentDB with MongoDB compatibility | Developers needing Databases operations with 10 tools | 10 endpoints vs 10 endpoints | auto / v2014-10-31 | View → |
| Amazon DynamoDB | Developers needing Databases operations with 10 tools | 10 endpoints vs 10 endpoints | auto / v2011-12-05 | View → |
9. Error Resolution & Troubleshooting Guide
Contextual diagnostics for HTTP status codes and JSON-RPC tool bridge operations.
-32600 (Invalid Request)Root Cause: Malformed JSON-RPC payload sent to local MCP bridge process.
Resolution Action: Verify MCP client payload adheres to JSON-RPC 2.0 specification.
-32601 (Method Not Found)Root Cause: Requested operation does not exist in mapped AWS Application Discovery Service OpenAPI endpoint schemas.
Resolution Action: Inspect Section 5 endpoints table to confirm valid method names and paths.
-32602 (Invalid Params)Root Cause: Missing or invalid parameters for target tool operation.
Resolution Action: Check parameter data types against OpenAPI JSON Schema specification.
429 Rate Limit ExceededRoot Cause: Upstream AWS Application Discovery Service API request rate limit quota reached.
Resolution Action: Implement exponential backoff in tool execution loop or verify provider plan quotas.
OPENAPI_GATEWAY_TIMEOUTRoot Cause: Upstream AWS Application Discovery Service endpoint response latency exceeded timeout threshold.
Resolution Action: Verify network connectivity and check provider system status dashboard.
Official Verified Sources for AWS Application Discovery Service
Authoritative upstream repositories, specifications, package registries, and configuration endpoints.
Official Upstream Documentation
Official developer documentation and API reference for AWS Application Discovery Service.
https://docs.aws.amazon.com/discovery/OpenAPI 3.0 Specification
Machine-readable OpenAPI schema source used for MCP tool mapping.
https://api.apis.guru/v2/specs/amazonaws.com/discovery/2015-11-01/openapi.jsonHosted MCPBridge Configuration
Pre-generated Model Context Protocol JSON configuration hosted on MCPBridge.
https://mcpbridge.org/config/amazonaws-com-discovery.jsonOpenAPI-to-MCP Converter Tool
Client-side browser converter to customize or filter endpoint tools.
https://mcpbridge.org/convert/Claim & Maintainer Verification
Submit a claim to verify API publisher ownership and update metadata.
https://github.com/stormlive-ai/mcp-bridge-docs/issues/new?title=Claim+Listing%3A+AWS+Application+Discovery+Service+%28api%3A+amazonaws-com-discovery%29&labels=claim-listing&body=%23%23+Claim+Listing+Request%0A%0AI+would+like+to+claim+this+listing%3A%0A%0A-+**Type%3A**+api%0A-+**ID%3A**+amazonaws-com-discovery%0A-+**Name%3A**+AWS+Application+Discovery+Service%0A%0A%23%23%23+Your+Information%0A%0A**GitHub+Handle%3A**+%3C%21--+your+GitHub+username+--%3E%0A%0A**Email%3A**+%3C%21--+optional%2C+for+verification+--%3E%0A%0A**Relationship+to+this+API%3A**%0A-+%5B+%5D+I+am+the+API+provider+%2F+maintainer%0A-+%5B+%5D+I+am+an+authorized+representative%0A-+%5B+%5D+Other%3A%0A%0A%23%23%23+Verification+Method%0A-+%5B+%5D+I+will+add+a+CNAME%2FTXT+record+to+verify+domain+ownership%0A-+%5B+%5D+I+can+confirm+from+an+email+address+at+the+provider+domain%0A-+%5B+%5D+I+maintain+the+GitHub+repository%0A%0A%23%23%23+Updates+I%27d+Like+to+Make+%28optional%29%0A%3C%21--+What+would+you+like+to+update%3F+Description%2C+links%2C+category%2C+etc.+--%3E%0A%0A---%0A*Submitted+via+MCP-Bridge+claim+form*Frequently Asked Technical Questions: AWS Application Discovery Service
Targeted developer questions regarding installation, client configuration, credentials, and error resolution.
The AWS Application Discovery Service MCP server connects AI coding assistants (Claude Desktop, Cursor, VS Code, Zed) to the AWS Application Discovery Service API using the Model Context Protocol. It converts 10 OpenAPI operations into native MCP tools callable during chat sessions.